diff --git a/project/DataPrep_EDA.ipynb b/project/DataPrep_EDA.ipynb
index e5b94f0..8436f12 100644
--- a/project/DataPrep_EDA.ipynb
+++ b/project/DataPrep_EDA.ipynb
@@ -37,52 +37,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-20T02:14:34.811557Z",
+     "start_time": "2024-11-20T02:14:34.804489Z"
+    }
+   },
    "source": [
     "# Importing Libraries\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns"
-   ]
+    "import seaborn as sns\n",
+    "import os\n",
+    "\n",
+    "from fastf1.ergast.structure import FastestLap"
+   ],
+   "outputs": [],
+   "execution_count": 9
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "core           INFO \tLoading data for Italian Grand Prix - Qualifying [v3.4.4]\n",
-      "req            INFO \tUsing cached data for session_info\n",
-      "req            INFO \tUsing cached data for driver_info\n",
-      "req            INFO \tUsing cached data for session_status_data\n",
-      "req            INFO \tUsing cached data for track_status_data\n",
-      "req            INFO \tUsing cached data for _extended_timing_data\n",
-      "req            INFO \tUsing cached data for timing_app_data\n",
-      "core           INFO \tProcessing timing data...\n",
-      "req            INFO \tUsing cached data for car_data\n",
-      "req            INFO \tUsing cached data for position_data\n",
-      "req            INFO \tUsing cached data for weather_data\n",
-      "req            INFO \tUsing cached data for race_control_messages\n",
-      "core           INFO \tFinished loading data for 20 drivers: ['16', '44', '77', '5', '3', '27', '55', '23', '18', '7', '99', '20', '26', '4', '10', '8', '11', '63', '88', '33']\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-20T02:14:38.532179Z",
+     "start_time": "2024-11-20T02:14:36.799495Z"
     }
-   ],
+   },
    "source": [
     "# FastF1 Example\n",
     "import fastf1\n",
@@ -106,7 +87,1694 @@
     "ax.set_title('Leclerc is')\n",
     "ax.legend()\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "core           INFO \tLoading data for Italian Grand Prix - Qualifying [v3.4.4]\n",
+      "req            INFO \tUsing cached data for session_info\n",
+      "req            INFO \tUsing cached data for driver_info\n",
+      "req            INFO \tUsing cached data for session_status_data\n",
+      "req            INFO \tUsing cached data for track_status_data\n",
+      "req            INFO \tUsing cached data for _extended_timing_data\n",
+      "req            INFO \tUsing cached data for timing_app_data\n",
+      "core           INFO \tProcessing timing data...\n",
+      "req            INFO \tUsing cached data for car_data\n",
+      "req            INFO \tUsing cached data for position_data\n",
+      "req            INFO \tUsing cached data for weather_data\n",
+      "req            INFO \tUsing cached data for race_control_messages\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['16', '44', '77', '5', '3', '27', '55', '23', '18', '7', '99', '20', '26', '4', '10', '8', '11', '63', '88', '33']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-20T03:31:25.964278Z",
+     "start_time": "2024-11-20T03:29:39.724591Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Define the cache directory\n",
+    "cache_dir = '/Users/connorcoles/PycharmProjects/F1-Prediction/csci349_final_project/project/cache'\n",
+    "if not os.path.exists(cache_dir):\n",
+    "    os.makedirs(cache_dir)\n",
+    "\n",
+    "fastf1.Cache.enable_cache(cache_dir)\n",
+    "\n",
+    "# Years and sessions of interest\n",
+    "years = [2020, 2021, 2022, 2023, 2024]\n",
+    "sessions = ['Q', 'Race']  # Qualifying and Race sessions\n",
+    "event_name = 'Bahrain'    # Example event name\n",
+    "\n",
+    "# Data holders\n",
+    "weather_data_list = []\n",
+    "lap_data_list = []\n",
+    "\n",
+    "# Loop through years and sessions\n",
+    "for year in years:\n",
+    "    for session_name in sessions:\n",
+    "        try:\n",
+    "            # Load the session\n",
+    "            session = fastf1.get_session(year, event_name, session_name)\n",
+    "            session.load()\n",
+    "            \n",
+    "            # Process weather data\n",
+    "            weather_data = session.weather_data\n",
+    "            weather_df = pd.DataFrame(weather_data)\n",
+    "            weather_df['Year'] = year\n",
+    "            weather_df['Session'] = session_name\n",
+    "            weather_data_list.append(weather_df)\n",
+    "\n",
+    "            # Process lap data\n",
+    "            lap_data = session.laps\n",
+    "            lap_df = pd.DataFrame(lap_data)\n",
+    "            lap_df['Year'] = year\n",
+    "            lap_df['Session'] = session_name\n",
+    "            lap_data_list.append(lap_df)\n",
+    "        \n",
+    "        except Exception as e:\n",
+    "            print(f\"Error with {event_name} {session_name} ({year}): {e}\")\n",
+    "\n",
+    "# Combine weather and lap data into separate DataFrames\n",
+    "if weather_data_list:\n",
+    "    weather_data_combined = pd.concat(weather_data_list, ignore_index=True)\n",
+    "    print(\"Weather Data:\")\n",
+    "    print(weather_data_combined.head())\n",
+    "\n",
+    "if lap_data_list:\n",
+    "    lap_data_combined = pd.concat(lap_data_list, ignore_index=True)\n",
+    "    print(\"Lap Data:\")\n",
+    "    print(lap_data_combined.head())"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "events      WARNING \tCorrecting user input 'Bahrain' to 'Bahrain Grand Prix'\n",
+      "core           INFO \tLoading data for Bahrain Grand Prix - Qualifying [v3.4.4]\n",
+      "req            INFO \tUsing cached data for session_info\n",
+      "req            INFO \tUsing cached data for driver_info\n",
+      "req            INFO \tUsing cached data for session_status_data\n",
+      "req            INFO \tUsing cached data for track_status_data\n",
+      "req            INFO \tUsing cached data for _extended_timing_data\n",
+      "req            INFO \tUsing cached data for timing_app_data\n",
+      "core           INFO \tProcessing timing data...\n",
+      "req            INFO \tUsing cached data for car_data\n",
+      "req            INFO \tUsing cached data for position_data\n",
+      "req            INFO \tUsing cached data for weather_data\n",
+      "req            INFO \tUsing cached data for race_control_messages\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['44', '77', '33', '23', '11', '3', '31', '10', '4', '26', '5', '16', '18', '63', '55', '99', '7', '20', '8', '6']\n",
+      "events      WARNING \tCorrecting user input 'Bahrain' to 'Bahrain Grand Prix'\n",
+      "core           INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
+      "req            INFO \tUsing cached data for session_info\n",
+      "req            INFO \tUsing cached data for driver_info\n",
+      "req            INFO \tUsing cached data for session_status_data\n",
+      "req            INFO \tUsing cached data for lap_count\n",
+      "req            INFO \tUsing cached data for track_status_data\n",
+      "req            INFO \tUsing cached data for _extended_timing_data\n",
+      "req            INFO \tUsing cached data for timing_app_data\n",
+      "core           INFO \tProcessing timing data...\n",
+      "req            INFO \tUsing cached data for car_data\n",
+      "req            INFO \tUpdating cache for position_data...\n",
+      "_api           INFO \tFetching position data...\n",
+      "_api           INFO \tParsing position data...\n",
+      "_api        WARNING \tDriver 241: Position data is incomplete!\n",
+      "_api        WARNING \tDriver 242: Position data is incomplete!\n",
+      "_api        WARNING \tDriver 243: Position data is incomplete!\n",
+      "req            INFO \tCache updated!\n",
+      "req            INFO \tNo cached data found for weather_data. Loading data...\n",
+      "_api           INFO \tFetching weather data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for race_control_messages. Loading data...\n",
+      "_api           INFO \tFetching race control messages...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['44', '33', '23', '4', '55', '10', '3', '77', '31', '16', '26', '63', '5', '6', '7', '99', '20', '11', '18', '8']\n",
+      "core           INFO \tLoading data for Bahrain Grand Prix - Qualifying [v3.4.4]\n",
+      "req            INFO \tUsing cached data for session_info\n",
+      "req            INFO \tUsing cached data for driver_info\n",
+      "req            INFO \tUsing cached data for session_status_data\n",
+      "req            INFO \tUsing cached data for track_status_data\n",
+      "req            INFO \tUsing cached data for _extended_timing_data\n",
+      "req            INFO \tUsing cached data for timing_app_data\n",
+      "core           INFO \tProcessing timing data...\n",
+      "req            INFO \tUsing cached data for car_data\n",
+      "req            INFO \tUsing cached data for position_data\n",
+      "req            INFO \tUsing cached data for weather_data\n",
+      "req            INFO \tUsing cached data for race_control_messages\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['33', '44', '77', '16', '10', '3', '4', '55', '14', '18', '11', '99', '22', '7', '63', '31', '6', '5', '47', '9']\n",
+      "core           INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
+      "req            INFO \tNo cached data found for session_info. Loading data...\n",
+      "_api           INFO \tFetching session info data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for driver_info. Loading data...\n",
+      "_api           INFO \tFetching driver list...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for session_status_data. Loading data...\n",
+      "_api           INFO \tFetching session status data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for lap_count. Loading data...\n",
+      "_api           INFO \tFetching lap count data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for track_status_data. Loading data...\n",
+      "_api           INFO \tFetching track status data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
+      "_api           INFO \tFetching timing data...\n",
+      "_api           INFO \tParsing timing data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for timing_app_data. Loading data...\n",
+      "_api           INFO \tFetching timing app data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "core           INFO \tProcessing timing data...\n",
+      "req            INFO \tNo cached data found for car_data. Loading data...\n",
+      "_api           INFO \tFetching car data...\n",
+      "_api           INFO \tParsing car data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for position_data. Loading data...\n",
+      "_api           INFO \tFetching position data...\n",
+      "_api           INFO \tParsing position data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for weather_data. Loading data...\n",
+      "_api           INFO \tFetching weather data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for race_control_messages. Loading data...\n",
+      "_api           INFO \tFetching race control messages...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['44', '33', '77', '4', '11', '16', '3', '55', '22', '18', '7', '99', '31', '63', '5', '47', '10', '6', '14', '9']\n",
+      "core           INFO \tLoading data for Bahrain Grand Prix - Qualifying [v3.4.4]\n",
+      "req            INFO \tUsing cached data for session_info\n",
+      "req            INFO \tUsing cached data for driver_info\n",
+      "req            INFO \tUsing cached data for session_status_data\n",
+      "req            INFO \tUsing cached data for track_status_data\n",
+      "req            INFO \tUsing cached data for _extended_timing_data\n",
+      "req            INFO \tUsing cached data for timing_app_data\n",
+      "core           INFO \tProcessing timing data...\n",
+      "req            INFO \tUsing cached data for car_data\n",
+      "req            INFO \tUsing cached data for position_data\n",
+      "req            INFO \tUsing cached data for weather_data\n",
+      "req            INFO \tUsing cached data for race_control_messages\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['16', '1', '55', '11', '44', '77', '20', '14', '63', '10', '31', '47', '4', '23', '24', '22', '27', '3', '18', '6']\n",
+      "core           INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
+      "req            INFO \tNo cached data found for session_info. Loading data...\n",
+      "_api           INFO \tFetching session info data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for driver_info. Loading data...\n",
+      "_api           INFO \tFetching driver list...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for session_status_data. Loading data...\n",
+      "_api           INFO \tFetching session status data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for lap_count. Loading data...\n",
+      "_api           INFO \tFetching lap count data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for track_status_data. Loading data...\n",
+      "_api           INFO \tFetching track status data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
+      "_api           INFO \tFetching timing data...\n",
+      "_api           INFO \tParsing timing data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for timing_app_data. Loading data...\n",
+      "_api           INFO \tFetching timing app data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "core           INFO \tProcessing timing data...\n",
+      "req            INFO \tNo cached data found for car_data. Loading data...\n",
+      "_api           INFO \tFetching car data...\n",
+      "_api           INFO \tParsing car data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for position_data. Loading data...\n",
+      "_api           INFO \tFetching position data...\n",
+      "_api           INFO \tParsing position data...\n",
+      "_api        WARNING \tDriver 241: Position data is incomplete!\n",
+      "_api        WARNING \tDriver 242: Position data is incomplete!\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for weather_data. Loading data...\n",
+      "_api           INFO \tFetching weather data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for race_control_messages. Loading data...\n",
+      "_api           INFO \tFetching race control messages...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['16', '55', '44', '63', '20', '77', '31', '22', '14', '24', '47', '18', '23', '3', '4', '6', '27', '11', '1', '10']\n",
+      "core           INFO \tLoading data for Bahrain Grand Prix - Qualifying [v3.4.4]\n",
+      "req            INFO \tUsing cached data for session_info\n",
+      "req            INFO \tUsing cached data for driver_info\n",
+      "req            INFO \tUsing cached data for session_status_data\n",
+      "req            INFO \tUsing cached data for track_status_data\n",
+      "req            INFO \tUsing cached data for _extended_timing_data\n",
+      "req            INFO \tUsing cached data for timing_app_data\n",
+      "core           INFO \tProcessing timing data...\n",
+      "req            INFO \tUsing cached data for car_data\n",
+      "req            INFO \tUsing cached data for position_data\n",
+      "req            INFO \tUsing cached data for weather_data\n",
+      "req            INFO \tUsing cached data for race_control_messages\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['1', '11', '16', '55', '14', '63', '44', '18', '31', '27', '4', '77', '24', '22', '23', '2', '20', '81', '21', '10']\n",
+      "core           INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
+      "req            INFO \tNo cached data found for session_info. Loading data...\n",
+      "_api           INFO \tFetching session info data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for driver_info. Loading data...\n",
+      "_api           INFO \tFetching driver list...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for session_status_data. Loading data...\n",
+      "_api           INFO \tFetching session status data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for lap_count. Loading data...\n",
+      "_api           INFO \tFetching lap count data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for track_status_data. Loading data...\n",
+      "_api           INFO \tFetching track status data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
+      "_api           INFO \tFetching timing data...\n",
+      "_api           INFO \tParsing timing data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for timing_app_data. Loading data...\n",
+      "_api           INFO \tFetching timing app data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "core           INFO \tProcessing timing data...\n",
+      "req            INFO \tNo cached data found for car_data. Loading data...\n",
+      "_api           INFO \tFetching car data...\n",
+      "_api           INFO \tParsing car data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for position_data. Loading data...\n",
+      "_api           INFO \tFetching position data...\n",
+      "_api           INFO \tParsing position data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for weather_data. Loading data...\n",
+      "_api           INFO \tFetching weather data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for race_control_messages. Loading data...\n",
+      "_api           INFO \tFetching race control messages...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['1', '11', '14', '55', '44', '18', '63', '77', '10', '23', '22', '2', '20', '21', '27', '24', '4', '31', '16', '81']\n",
+      "core           INFO \tLoading data for Bahrain Grand Prix - Qualifying [v3.4.4]\n",
+      "req            INFO \tUsing cached data for session_info\n",
+      "req            INFO \tUsing cached data for driver_info\n",
+      "req            INFO \tUsing cached data for session_status_data\n",
+      "req            INFO \tUsing cached data for track_status_data\n",
+      "req            INFO \tUsing cached data for _extended_timing_data\n",
+      "req            INFO \tUsing cached data for timing_app_data\n",
+      "core           INFO \tProcessing timing data...\n",
+      "req            INFO \tUsing cached data for car_data\n",
+      "req            INFO \tUsing cached data for position_data\n",
+      "req            INFO \tUsing cached data for weather_data\n",
+      "req            INFO \tUsing cached data for race_control_messages\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['1', '16', '63', '55', '11', '14', '4', '81', '44', '27', '22', '18', '23', '3', '20', '77', '24', '2', '31', '10']\n",
+      "core           INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
+      "req            INFO \tNo cached data found for session_info. Loading data...\n",
+      "_api           INFO \tFetching session info data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for driver_info. Loading data...\n",
+      "_api           INFO \tFetching driver list...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for session_status_data. Loading data...\n",
+      "_api           INFO \tFetching session status data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for lap_count. Loading data...\n",
+      "_api           INFO \tFetching lap count data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for track_status_data. Loading data...\n",
+      "_api           INFO \tFetching track status data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
+      "_api           INFO \tFetching timing data...\n",
+      "_api           INFO \tParsing timing data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for timing_app_data. Loading data...\n",
+      "_api           INFO \tFetching timing app data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "core           INFO \tProcessing timing data...\n",
+      "logger      WARNING \tFailed to add first lap time from Ergast!\n",
+      "req            INFO \tNo cached data found for car_data. Loading data...\n",
+      "_api           INFO \tFetching car data...\n",
+      "_api           INFO \tParsing car data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for position_data. Loading data...\n",
+      "_api           INFO \tFetching position data...\n",
+      "_api           INFO \tParsing position data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for weather_data. Loading data...\n",
+      "_api           INFO \tFetching weather data...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "req            INFO \tNo cached data found for race_control_messages. Loading data...\n",
+      "_api           INFO \tFetching race control messages...\n",
+      "req            INFO \tData has been written to cache!\n",
+      "core           INFO \tFinished loading data for 20 drivers: ['1', '11', '55', '16', '63', '4', '44', '81', '14', '18', '24', '20', '3', '22', '23', '27', '31', '10', '77', '2']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Weather Data:\n",
+      "                    Time  AirTemp  Humidity  Pressure  Rainfall  TrackTemp  \\\n",
+      "0 0 days 00:00:33.157000     26.9      52.6    1015.9     False       28.7   \n",
+      "1 0 days 00:01:33.168000     26.9      52.7    1016.0     False       28.6   \n",
+      "2 0 days 00:02:33.172000     26.8      52.8    1015.9     False       28.5   \n",
+      "3 0 days 00:03:33.168000     26.8      53.0    1015.9     False       28.5   \n",
+      "4 0 days 00:04:33.155000     26.7      53.2    1016.0     False       28.5   \n",
+      "\n",
+      "   WindDirection  WindSpeed  Year Session  \n",
+      "0            305        0.6  2020       Q  \n",
+      "1             40        0.8  2020       Q  \n",
+      "2            341        0.8  2020       Q  \n",
+      "3            295        0.4  2020       Q  \n",
+      "4            347        0.5  2020       Q  \n",
+      "Lap Data:\n",
+      "                    Time Driver DriverNumber                LapTime  \\\n",
+      "0 0 days 00:23:28.426000    HAM           44                    NaT   \n",
+      "1 0 days 00:24:56.769000    HAM           44 0 days 00:01:28.343000   \n",
+      "2 0 days 00:26:46.183000    HAM           44 0 days 00:01:49.414000   \n",
+      "3 0 days 00:32:41.745000    HAM           44                    NaT   \n",
+      "4 0 days 00:34:21.973000    HAM           44 0 days 00:01:40.228000   \n",
+      "\n",
+      "   LapNumber  Stint             PitOutTime              PitInTime  \\\n",
+      "0        1.0    1.0 0 days 00:21:22.161000                    NaT   \n",
+      "1        2.0    1.0                    NaT                    NaT   \n",
+      "2        3.0    1.0                    NaT 0 days 00:26:44.401000   \n",
+      "3        4.0    2.0 0 days 00:30:17.211000                    NaT   \n",
+      "4        5.0    2.0                    NaT 0 days 00:34:20.228000   \n",
+      "\n",
+      "             Sector1Time            Sector2Time  ...           LapStartTime  \\\n",
+      "0                    NaT 0 days 00:00:57.104000  ... 0 days 00:21:22.161000   \n",
+      "1 0 days 00:00:28.083000 0 days 00:00:38.020000  ... 0 days 00:23:28.426000   \n",
+      "2 0 days 00:00:34.081000 0 days 00:00:45.383000  ... 0 days 00:24:56.769000   \n",
+      "3                    NaT 0 days 00:01:06.133000  ... 0 days 00:26:46.183000   \n",
+      "4 0 days 00:00:28.239000 0 days 00:00:45.630000  ... 0 days 00:32:41.745000   \n",
+      "\n",
+      "             LapStartDate TrackStatus Position  Deleted  DeletedReason  \\\n",
+      "0 2020-11-28 14:06:22.193           1      NaN    False                  \n",
+      "1 2020-11-28 14:08:28.458           1      NaN    False                  \n",
+      "2 2020-11-28 14:09:56.801           1      NaN    False                  \n",
+      "3 2020-11-28 14:11:46.215           1      NaN    False                  \n",
+      "4 2020-11-28 14:17:41.777           1      NaN    False                  \n",
+      "\n",
+      "   FastF1Generated  IsAccurate  Year Session  \n",
+      "0            False       False  2020       Q  \n",
+      "1            False        True  2020       Q  \n",
+      "2            False       False  2020       Q  \n",
+      "3            False       False  2020       Q  \n",
+      "4            False       False  2020       Q  \n",
+      "\n",
+      "[5 rows x 33 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 30
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-20T03:39:01.456623Z",
+     "start_time": "2024-11-20T03:39:01.428009Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Display data\n",
+    "weather_data_combined.head(5)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                    Time  AirTemp  Humidity  Pressure  Rainfall  TrackTemp  \\\n",
+       "0 0 days 00:00:33.157000     26.9      52.6    1015.9     False       28.7   \n",
+       "1 0 days 00:01:33.168000     26.9      52.7    1016.0     False       28.6   \n",
+       "2 0 days 00:02:33.172000     26.8      52.8    1015.9     False       28.5   \n",
+       "3 0 days 00:03:33.168000     26.8      53.0    1015.9     False       28.5   \n",
+       "4 0 days 00:04:33.155000     26.7      53.2    1016.0     False       28.5   \n",
+       "\n",
+       "   WindDirection  WindSpeed  Year Session  \n",
+       "0            305        0.6  2020       Q  \n",
+       "1             40        0.8  2020       Q  \n",
+       "2            341        0.8  2020       Q  \n",
+       "3            295        0.4  2020       Q  \n",
+       "4            347        0.5  2020       Q  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Time</th>\n",
+       "      <th>AirTemp</th>\n",
+       "      <th>Humidity</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>Rainfall</th>\n",
+       "      <th>TrackTemp</th>\n",
+       "      <th>WindDirection</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Session</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0 days 00:00:33.157000</td>\n",
+       "      <td>26.9</td>\n",
+       "      <td>52.6</td>\n",
+       "      <td>1015.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>28.7</td>\n",
+       "      <td>305</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0 days 00:01:33.168000</td>\n",
+       "      <td>26.9</td>\n",
+       "      <td>52.7</td>\n",
+       "      <td>1016.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>28.6</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0 days 00:02:33.172000</td>\n",
+       "      <td>26.8</td>\n",
+       "      <td>52.8</td>\n",
+       "      <td>1015.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>28.5</td>\n",
+       "      <td>341</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0 days 00:03:33.168000</td>\n",
+       "      <td>26.8</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>1015.9</td>\n",
+       "      <td>False</td>\n",
+       "      <td>28.5</td>\n",
+       "      <td>295</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0 days 00:04:33.155000</td>\n",
+       "      <td>26.7</td>\n",
+       "      <td>53.2</td>\n",
+       "      <td>1016.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>28.5</td>\n",
+       "      <td>347</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 47
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-20T04:00:16.003135Z",
+     "start_time": "2024-11-20T04:00:15.970644Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "lap_data_combined.head(5)",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                    Time Driver DriverNumber                LapTime  \\\n",
+       "0 0 days 00:23:28.426000    HAM           44                    NaT   \n",
+       "1 0 days 00:24:56.769000    HAM           44 0 days 00:01:28.343000   \n",
+       "2 0 days 00:26:46.183000    HAM           44 0 days 00:01:49.414000   \n",
+       "3 0 days 00:32:41.745000    HAM           44                    NaT   \n",
+       "4 0 days 00:34:21.973000    HAM           44 0 days 00:01:40.228000   \n",
+       "\n",
+       "   LapNumber  Stint             PitOutTime              PitInTime  \\\n",
+       "0        1.0    1.0 0 days 00:21:22.161000                    NaT   \n",
+       "1        2.0    1.0                    NaT                    NaT   \n",
+       "2        3.0    1.0                    NaT 0 days 00:26:44.401000   \n",
+       "3        4.0    2.0 0 days 00:30:17.211000                    NaT   \n",
+       "4        5.0    2.0                    NaT 0 days 00:34:20.228000   \n",
+       "\n",
+       "             Sector1Time            Sector2Time  ...           LapStartTime  \\\n",
+       "0                    NaT 0 days 00:00:57.104000  ... 0 days 00:21:22.161000   \n",
+       "1 0 days 00:00:28.083000 0 days 00:00:38.020000  ... 0 days 00:23:28.426000   \n",
+       "2 0 days 00:00:34.081000 0 days 00:00:45.383000  ... 0 days 00:24:56.769000   \n",
+       "3                    NaT 0 days 00:01:06.133000  ... 0 days 00:26:46.183000   \n",
+       "4 0 days 00:00:28.239000 0 days 00:00:45.630000  ... 0 days 00:32:41.745000   \n",
+       "\n",
+       "             LapStartDate TrackStatus Position  Deleted  DeletedReason  \\\n",
+       "0 2020-11-28 14:06:22.193           1      NaN    False                  \n",
+       "1 2020-11-28 14:08:28.458           1      NaN    False                  \n",
+       "2 2020-11-28 14:09:56.801           1      NaN    False                  \n",
+       "3 2020-11-28 14:11:46.215           1      NaN    False                  \n",
+       "4 2020-11-28 14:17:41.777           1      NaN    False                  \n",
+       "\n",
+       "   FastF1Generated  IsAccurate  Year Session  \n",
+       "0            False       False  2020       Q  \n",
+       "1            False        True  2020       Q  \n",
+       "2            False       False  2020       Q  \n",
+       "3            False       False  2020       Q  \n",
+       "4            False       False  2020       Q  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Driver</th>\n",
+       "      <th>DriverNumber</th>\n",
+       "      <th>LapTime</th>\n",
+       "      <th>LapNumber</th>\n",
+       "      <th>Stint</th>\n",
+       "      <th>PitOutTime</th>\n",
+       "      <th>PitInTime</th>\n",
+       "      <th>Sector1Time</th>\n",
+       "      <th>Sector2Time</th>\n",
+       "      <th>...</th>\n",
+       "      <th>LapStartTime</th>\n",
+       "      <th>LapStartDate</th>\n",
+       "      <th>TrackStatus</th>\n",
+       "      <th>Position</th>\n",
+       "      <th>Deleted</th>\n",
+       "      <th>DeletedReason</th>\n",
+       "      <th>FastF1Generated</th>\n",
+       "      <th>IsAccurate</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Session</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0 days 00:23:28.426000</td>\n",
+       "      <td>HAM</td>\n",
+       "      <td>44</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0 days 00:21:22.161000</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>0 days 00:00:57.104000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 00:21:22.161000</td>\n",
+       "      <td>2020-11-28 14:06:22.193</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0 days 00:24:56.769000</td>\n",
+       "      <td>HAM</td>\n",
+       "      <td>44</td>\n",
+       "      <td>0 days 00:01:28.343000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>0 days 00:00:28.083000</td>\n",
+       "      <td>0 days 00:00:38.020000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 00:23:28.426000</td>\n",
+       "      <td>2020-11-28 14:08:28.458</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0 days 00:26:46.183000</td>\n",
+       "      <td>HAM</td>\n",
+       "      <td>44</td>\n",
+       "      <td>0 days 00:01:49.414000</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>0 days 00:26:44.401000</td>\n",
+       "      <td>0 days 00:00:34.081000</td>\n",
+       "      <td>0 days 00:00:45.383000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 00:24:56.769000</td>\n",
+       "      <td>2020-11-28 14:09:56.801</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0 days 00:32:41.745000</td>\n",
+       "      <td>HAM</td>\n",
+       "      <td>44</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0 days 00:30:17.211000</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>0 days 00:01:06.133000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 00:26:46.183000</td>\n",
+       "      <td>2020-11-28 14:11:46.215</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0 days 00:34:21.973000</td>\n",
+       "      <td>HAM</td>\n",
+       "      <td>44</td>\n",
+       "      <td>0 days 00:01:40.228000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>0 days 00:34:20.228000</td>\n",
+       "      <td>0 days 00:00:28.239000</td>\n",
+       "      <td>0 days 00:00:45.630000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 00:32:41.745000</td>\n",
+       "      <td>2020-11-28 14:17:41.777</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 67
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-20T03:42:50.147348Z",
+     "start_time": "2024-11-20T03:42:50.070096Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#What does our data look like?\n",
+    "weather_data_combined.info()\n",
+    "lap_data_combined.info()\n",
+    "\n",
+    "#How many unique values do we have?\n",
+    "print(weather_data_combined.nunique())\n",
+    "print(lap_data_combined.nunique())\n",
+    "\n",
+    "#Are there any missing values?\n",
+    "print(weather_data_combined.isnull().sum())\n",
+    "print(lap_data_combined.isnull().sum())"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1244 entries, 0 to 1243\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column         Non-Null Count  Dtype          \n",
+      "---  ------         --------------  -----          \n",
+      " 0   Time           1244 non-null   timedelta64[ns]\n",
+      " 1   AirTemp        1244 non-null   float64        \n",
+      " 2   Humidity       1244 non-null   float64        \n",
+      " 3   Pressure       1244 non-null   float64        \n",
+      " 4   Rainfall       1244 non-null   bool           \n",
+      " 5   TrackTemp      1244 non-null   float64        \n",
+      " 6   WindDirection  1244 non-null   int64          \n",
+      " 7   WindSpeed      1244 non-null   float64        \n",
+      " 8   Year           1244 non-null   int64          \n",
+      " 9   Session        1244 non-null   object         \n",
+      "dtypes: bool(1), float64(5), int64(2), object(1), timedelta64[ns](1)\n",
+      "memory usage: 88.8+ KB\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 6628 entries, 0 to 6627\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column              Non-Null Count  Dtype          \n",
+      "---  ------              --------------  -----          \n",
+      " 0   Time                6628 non-null   timedelta64[ns]\n",
+      " 1   Driver              6628 non-null   object         \n",
+      " 2   DriverNumber        6628 non-null   object         \n",
+      " 3   LapTime             6038 non-null   timedelta64[ns]\n",
+      " 4   LapNumber           6628 non-null   float64        \n",
+      " 5   Stint               6628 non-null   float64        \n",
+      " 6   PitOutTime          689 non-null    timedelta64[ns]\n",
+      " 7   PitInTime           694 non-null    timedelta64[ns]\n",
+      " 8   Sector1Time         6056 non-null   timedelta64[ns]\n",
+      " 9   Sector2Time         6599 non-null   timedelta64[ns]\n",
+      " 10  Sector3Time         6566 non-null   timedelta64[ns]\n",
+      " 11  Sector1SessionTime  6048 non-null   timedelta64[ns]\n",
+      " 12  Sector2SessionTime  6599 non-null   timedelta64[ns]\n",
+      " 13  Sector3SessionTime  6566 non-null   timedelta64[ns]\n",
+      " 14  SpeedI1             5394 non-null   float64        \n",
+      " 15  SpeedI2             6601 non-null   float64        \n",
+      " 16  SpeedFL             5926 non-null   float64        \n",
+      " 17  SpeedST             5944 non-null   float64        \n",
+      " 18  IsPersonalBest      6622 non-null   object         \n",
+      " 19  Compound            6628 non-null   object         \n",
+      " 20  TyreLife            6628 non-null   float64        \n",
+      " 21  FreshTyre           6628 non-null   bool           \n",
+      " 22  Team                6628 non-null   object         \n",
+      " 23  LapStartTime        6628 non-null   timedelta64[ns]\n",
+      " 24  LapStartDate        6622 non-null   datetime64[ns] \n",
+      " 25  TrackStatus         6628 non-null   object         \n",
+      " 26  Position            5349 non-null   float64        \n",
+      " 27  Deleted             6628 non-null   bool           \n",
+      " 28  DeletedReason       6622 non-null   object         \n",
+      " 29  FastF1Generated     6628 non-null   bool           \n",
+      " 30  IsAccurate          6628 non-null   bool           \n",
+      " 31  Year                6628 non-null   int64          \n",
+      " 32  Session             6628 non-null   object         \n",
+      "dtypes: bool(4), datetime64[ns](1), float64(8), int64(1), object(8), timedelta64[ns](11)\n",
+      "memory usage: 1.5+ MB\n",
+      "Time             1244\n",
+      "AirTemp           107\n",
+      "Humidity          240\n",
+      "Pressure           62\n",
+      "Rainfall            1\n",
+      "TrackTemp         144\n",
+      "WindDirection     301\n",
+      "WindSpeed          31\n",
+      "Year                5\n",
+      "Session             2\n",
+      "dtype: int64\n",
+      "Time                  6622\n",
+      "Driver                  29\n",
+      "DriverNumber            30\n",
+      "LapTime               4793\n",
+      "LapNumber               57\n",
+      "Stint                    7\n",
+      "PitOutTime             689\n",
+      "PitInTime              694\n",
+      "Sector1Time           3257\n",
+      "Sector2Time           4280\n",
+      "Sector3Time           3395\n",
+      "Sector1SessionTime    6043\n",
+      "Sector2SessionTime    6598\n",
+      "Sector3SessionTime    6558\n",
+      "SpeedI1                175\n",
+      "SpeedI2                204\n",
+      "SpeedFL                171\n",
+      "SpeedST                288\n",
+      "IsPersonalBest           2\n",
+      "Compound                 3\n",
+      "TyreLife                37\n",
+      "FreshTyre                2\n",
+      "Team                    15\n",
+      "LapStartTime          6511\n",
+      "LapStartDate          6509\n",
+      "TrackStatus             18\n",
+      "Position                20\n",
+      "Deleted                  2\n",
+      "DeletedReason           38\n",
+      "FastF1Generated          2\n",
+      "IsAccurate               2\n",
+      "Year                     5\n",
+      "Session                  2\n",
+      "dtype: int64\n",
+      "Time             0\n",
+      "AirTemp          0\n",
+      "Humidity         0\n",
+      "Pressure         0\n",
+      "Rainfall         0\n",
+      "TrackTemp        0\n",
+      "WindDirection    0\n",
+      "WindSpeed        0\n",
+      "Year             0\n",
+      "Session          0\n",
+      "dtype: int64\n",
+      "Time                     0\n",
+      "Driver                   0\n",
+      "DriverNumber             0\n",
+      "LapTime                590\n",
+      "LapNumber                0\n",
+      "Stint                    0\n",
+      "PitOutTime            5939\n",
+      "PitInTime             5934\n",
+      "Sector1Time            572\n",
+      "Sector2Time             29\n",
+      "Sector3Time             62\n",
+      "Sector1SessionTime     580\n",
+      "Sector2SessionTime      29\n",
+      "Sector3SessionTime      62\n",
+      "SpeedI1               1234\n",
+      "SpeedI2                 27\n",
+      "SpeedFL                702\n",
+      "SpeedST                684\n",
+      "IsPersonalBest           6\n",
+      "Compound                 0\n",
+      "TyreLife                 0\n",
+      "FreshTyre                0\n",
+      "Team                     0\n",
+      "LapStartTime             0\n",
+      "LapStartDate             6\n",
+      "TrackStatus              0\n",
+      "Position              1279\n",
+      "Deleted                  0\n",
+      "DeletedReason            6\n",
+      "FastF1Generated          0\n",
+      "IsAccurate               0\n",
+      "Year                     0\n",
+      "Session                  0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "execution_count": 54
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-20T03:38:18.767017Z",
+     "start_time": "2024-11-20T03:38:18.692890Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#Describe the data\n",
+    "weather_data_combined.describe()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                            Time                    LapTime    LapNumber  \\\n",
+       "count                       6628                       6038  6628.000000   \n",
+       "mean   0 days 01:42:02.192371303  0 days 00:01:41.108333885    24.366777   \n",
+       "min       0 days 00:15:27.765000     0 days 00:01:27.264000     1.000000   \n",
+       "25%       0 days 01:09:17.505500     0 days 00:01:36.217000     9.000000   \n",
+       "50%       0 days 01:39:58.302000     0 days 00:01:37.859500    22.000000   \n",
+       "75%       0 days 02:13:43.248500     0 days 00:01:40.345500    39.000000   \n",
+       "max       0 days 03:33:47.428000     0 days 00:03:05.092000    57.000000   \n",
+       "std    0 days 00:44:32.891277624  0 days 00:00:10.588901657    16.860094   \n",
+       "\n",
+       "             Stint                 PitOutTime                  PitInTime  \\\n",
+       "count  6628.000000                        689                        694   \n",
+       "mean      2.545866  0 days 01:07:15.479603773  0 days 01:08:51.778342939   \n",
+       "min       1.000000     0 days 00:13:35.553000     0 days 00:18:28.415000   \n",
+       "25%       2.000000     0 days 00:29:59.107000     0 days 00:35:37.662250   \n",
+       "50%       2.000000     0 days 00:59:30.380000     0 days 00:58:21.241500   \n",
+       "75%       3.000000     0 days 01:28:09.343000     0 days 01:25:50.796000   \n",
+       "max       7.000000     0 days 03:28:04.389000     0 days 03:27:38.638000   \n",
+       "std       1.155031  0 days 00:41:40.584927846  0 days 00:39:18.227030052   \n",
+       "\n",
+       "                     Sector1Time                Sector2Time  \\\n",
+       "count                       6056                       6599   \n",
+       "mean   0 days 00:00:32.801727873  0 days 00:00:44.382851038   \n",
+       "min       0 days 00:00:27.669000     0 days 00:00:37.715000   \n",
+       "25%       0 days 00:00:30.732750     0 days 00:00:41.676500   \n",
+       "50%       0 days 00:00:31.148000     0 days 00:00:42.582000   \n",
+       "75%       0 days 00:00:31.792000     0 days 00:00:43.779500   \n",
+       "max       0 days 00:01:39.160000     0 days 00:01:27.340000   \n",
+       "std    0 days 00:00:05.843587609  0 days 00:00:06.025195453   \n",
+       "\n",
+       "                     Sector3Time         Sector1SessionTime  ...  \\\n",
+       "count                       6566                       6048  ...   \n",
+       "mean   0 days 00:00:25.863896740  0 days 01:45:48.699285218  ...   \n",
+       "min       0 days 00:00:21.853000     0 days 00:15:57.525000  ...   \n",
+       "25%       0 days 00:00:23.736000     0 days 01:14:22.595250  ...   \n",
+       "50%       0 days 00:00:24.126000     0 days 01:44:32.815000  ...   \n",
+       "75%       0 days 00:00:24.901750     0 days 02:15:42.518250  ...   \n",
+       "max       0 days 00:01:10.478000     0 days 03:32:33.946000  ...   \n",
+       "std    0 days 00:00:04.862155415  0 days 00:42:50.934311129  ...   \n",
+       "\n",
+       "              Sector3SessionTime      SpeedI1      SpeedI2      SpeedFL  \\\n",
+       "count                       6566  5394.000000  6601.000000  5926.000000   \n",
+       "mean   0 days 01:42:19.752332013   221.138673   240.172095   277.101249   \n",
+       "min       0 days 00:15:28.005000    54.000000    44.000000    42.000000   \n",
+       "25%       0 days 01:09:21.996000   225.000000   241.000000   277.000000   \n",
+       "50%       0 days 01:40:39.423500   231.000000   250.000000   281.000000   \n",
+       "75%       0 days 02:14:05.441000   235.000000   257.000000   284.000000   \n",
+       "max       0 days 03:33:47.428000   248.000000   274.000000   302.000000   \n",
+       "std    0 days 00:44:32.425629872    28.861242    32.657155    22.133798   \n",
+       "\n",
+       "           SpeedST     TyreLife               LapStartTime  \\\n",
+       "count  5944.000000  6628.000000                       6628   \n",
+       "mean    276.023890     8.922299  0 days 01:40:00.888514634   \n",
+       "min      31.000000     1.000000     0 days 00:13:35.553000   \n",
+       "25%     280.000000     3.000000     0 days 01:06:55.995500   \n",
+       "50%     295.000000     8.000000     0 days 01:38:20.161000   \n",
+       "75%     303.000000    13.000000     0 days 02:12:04.575250   \n",
+       "max     333.000000    37.000000     0 days 03:32:00.121000   \n",
+       "std      52.878471     6.475231  0 days 00:44:57.137961013   \n",
+       "\n",
+       "                        LapStartDate     Position         Year  \n",
+       "count                           6622  5349.000000  6628.000000  \n",
+       "mean   2022-05-19 11:52:27.328777728     9.980183  2022.045112  \n",
+       "min       2020-11-28 14:00:03.421000     1.000000  2020.000000  \n",
+       "25%    2021-03-28 15:21:05.414749952     5.000000  2021.000000  \n",
+       "50%    2022-03-20 15:46:26.377999872    10.000000  2022.000000  \n",
+       "75%       2023-03-05 16:12:27.612000    15.000000  2023.000000  \n",
+       "max       2024-03-02 16:35:23.280000    20.000000  2024.000000  \n",
+       "std                              NaN     5.511766     1.411731  \n",
+       "\n",
+       "[8 rows x 21 columns]"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Time</th>\n",
+       "      <th>LapTime</th>\n",
+       "      <th>LapNumber</th>\n",
+       "      <th>Stint</th>\n",
+       "      <th>PitOutTime</th>\n",
+       "      <th>PitInTime</th>\n",
+       "      <th>Sector1Time</th>\n",
+       "      <th>Sector2Time</th>\n",
+       "      <th>Sector3Time</th>\n",
+       "      <th>Sector1SessionTime</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Sector3SessionTime</th>\n",
+       "      <th>SpeedI1</th>\n",
+       "      <th>SpeedI2</th>\n",
+       "      <th>SpeedFL</th>\n",
+       "      <th>SpeedST</th>\n",
+       "      <th>TyreLife</th>\n",
+       "      <th>LapStartTime</th>\n",
+       "      <th>LapStartDate</th>\n",
+       "      <th>Position</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>6628</td>\n",
+       "      <td>6038</td>\n",
+       "      <td>6628.000000</td>\n",
+       "      <td>6628.000000</td>\n",
+       "      <td>689</td>\n",
+       "      <td>694</td>\n",
+       "      <td>6056</td>\n",
+       "      <td>6599</td>\n",
+       "      <td>6566</td>\n",
+       "      <td>6048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6566</td>\n",
+       "      <td>5394.000000</td>\n",
+       "      <td>6601.000000</td>\n",
+       "      <td>5926.000000</td>\n",
+       "      <td>5944.000000</td>\n",
+       "      <td>6628.000000</td>\n",
+       "      <td>6628</td>\n",
+       "      <td>6622</td>\n",
+       "      <td>5349.000000</td>\n",
+       "      <td>6628.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0 days 01:42:02.192371303</td>\n",
+       "      <td>0 days 00:01:41.108333885</td>\n",
+       "      <td>24.366777</td>\n",
+       "      <td>2.545866</td>\n",
+       "      <td>0 days 01:07:15.479603773</td>\n",
+       "      <td>0 days 01:08:51.778342939</td>\n",
+       "      <td>0 days 00:00:32.801727873</td>\n",
+       "      <td>0 days 00:00:44.382851038</td>\n",
+       "      <td>0 days 00:00:25.863896740</td>\n",
+       "      <td>0 days 01:45:48.699285218</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 01:42:19.752332013</td>\n",
+       "      <td>221.138673</td>\n",
+       "      <td>240.172095</td>\n",
+       "      <td>277.101249</td>\n",
+       "      <td>276.023890</td>\n",
+       "      <td>8.922299</td>\n",
+       "      <td>0 days 01:40:00.888514634</td>\n",
+       "      <td>2022-05-19 11:52:27.328777728</td>\n",
+       "      <td>9.980183</td>\n",
+       "      <td>2022.045112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0 days 00:15:27.765000</td>\n",
+       "      <td>0 days 00:01:27.264000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0 days 00:13:35.553000</td>\n",
+       "      <td>0 days 00:18:28.415000</td>\n",
+       "      <td>0 days 00:00:27.669000</td>\n",
+       "      <td>0 days 00:00:37.715000</td>\n",
+       "      <td>0 days 00:00:21.853000</td>\n",
+       "      <td>0 days 00:15:57.525000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 00:15:28.005000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>42.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0 days 00:13:35.553000</td>\n",
+       "      <td>2020-11-28 14:00:03.421000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2020.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0 days 01:09:17.505500</td>\n",
+       "      <td>0 days 00:01:36.217000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0 days 00:29:59.107000</td>\n",
+       "      <td>0 days 00:35:37.662250</td>\n",
+       "      <td>0 days 00:00:30.732750</td>\n",
+       "      <td>0 days 00:00:41.676500</td>\n",
+       "      <td>0 days 00:00:23.736000</td>\n",
+       "      <td>0 days 01:14:22.595250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 01:09:21.996000</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>241.000000</td>\n",
+       "      <td>277.000000</td>\n",
+       "      <td>280.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0 days 01:06:55.995500</td>\n",
+       "      <td>2021-03-28 15:21:05.414749952</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2021.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0 days 01:39:58.302000</td>\n",
+       "      <td>0 days 00:01:37.859500</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0 days 00:59:30.380000</td>\n",
+       "      <td>0 days 00:58:21.241500</td>\n",
+       "      <td>0 days 00:00:31.148000</td>\n",
+       "      <td>0 days 00:00:42.582000</td>\n",
+       "      <td>0 days 00:00:24.126000</td>\n",
+       "      <td>0 days 01:44:32.815000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 01:40:39.423500</td>\n",
+       "      <td>231.000000</td>\n",
+       "      <td>250.000000</td>\n",
+       "      <td>281.000000</td>\n",
+       "      <td>295.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>0 days 01:38:20.161000</td>\n",
+       "      <td>2022-03-20 15:46:26.377999872</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0 days 02:13:43.248500</td>\n",
+       "      <td>0 days 00:01:40.345500</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0 days 01:28:09.343000</td>\n",
+       "      <td>0 days 01:25:50.796000</td>\n",
+       "      <td>0 days 00:00:31.792000</td>\n",
+       "      <td>0 days 00:00:43.779500</td>\n",
+       "      <td>0 days 00:00:24.901750</td>\n",
+       "      <td>0 days 02:15:42.518250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 02:14:05.441000</td>\n",
+       "      <td>235.000000</td>\n",
+       "      <td>257.000000</td>\n",
+       "      <td>284.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0 days 02:12:04.575250</td>\n",
+       "      <td>2023-03-05 16:12:27.612000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>2023.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0 days 03:33:47.428000</td>\n",
+       "      <td>0 days 00:03:05.092000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>0 days 03:28:04.389000</td>\n",
+       "      <td>0 days 03:27:38.638000</td>\n",
+       "      <td>0 days 00:01:39.160000</td>\n",
+       "      <td>0 days 00:01:27.340000</td>\n",
+       "      <td>0 days 00:01:10.478000</td>\n",
+       "      <td>0 days 03:32:33.946000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 03:33:47.428000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>274.000000</td>\n",
+       "      <td>302.000000</td>\n",
+       "      <td>333.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0 days 03:32:00.121000</td>\n",
+       "      <td>2024-03-02 16:35:23.280000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>2024.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0 days 00:44:32.891277624</td>\n",
+       "      <td>0 days 00:00:10.588901657</td>\n",
+       "      <td>16.860094</td>\n",
+       "      <td>1.155031</td>\n",
+       "      <td>0 days 00:41:40.584927846</td>\n",
+       "      <td>0 days 00:39:18.227030052</td>\n",
+       "      <td>0 days 00:00:05.843587609</td>\n",
+       "      <td>0 days 00:00:06.025195453</td>\n",
+       "      <td>0 days 00:00:04.862155415</td>\n",
+       "      <td>0 days 00:42:50.934311129</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 00:44:32.425629872</td>\n",
+       "      <td>28.861242</td>\n",
+       "      <td>32.657155</td>\n",
+       "      <td>22.133798</td>\n",
+       "      <td>52.878471</td>\n",
+       "      <td>6.475231</td>\n",
+       "      <td>0 days 00:44:57.137961013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.511766</td>\n",
+       "      <td>1.411731</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 21 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 43
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-20T03:39:18.917017Z",
+     "start_time": "2024-11-20T03:39:18.855681Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "lap_data_combined.describe()",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                            Time                    LapTime    LapNumber  \\\n",
+       "count                       6628                       6038  6628.000000   \n",
+       "mean   0 days 01:42:02.192371303  0 days 00:01:41.108333885    24.366777   \n",
+       "min       0 days 00:15:27.765000     0 days 00:01:27.264000     1.000000   \n",
+       "25%       0 days 01:09:17.505500     0 days 00:01:36.217000     9.000000   \n",
+       "50%       0 days 01:39:58.302000     0 days 00:01:37.859500    22.000000   \n",
+       "75%       0 days 02:13:43.248500     0 days 00:01:40.345500    39.000000   \n",
+       "max       0 days 03:33:47.428000     0 days 00:03:05.092000    57.000000   \n",
+       "std    0 days 00:44:32.891277624  0 days 00:00:10.588901657    16.860094   \n",
+       "\n",
+       "             Stint                 PitOutTime                  PitInTime  \\\n",
+       "count  6628.000000                        689                        694   \n",
+       "mean      2.545866  0 days 01:07:15.479603773  0 days 01:08:51.778342939   \n",
+       "min       1.000000     0 days 00:13:35.553000     0 days 00:18:28.415000   \n",
+       "25%       2.000000     0 days 00:29:59.107000     0 days 00:35:37.662250   \n",
+       "50%       2.000000     0 days 00:59:30.380000     0 days 00:58:21.241500   \n",
+       "75%       3.000000     0 days 01:28:09.343000     0 days 01:25:50.796000   \n",
+       "max       7.000000     0 days 03:28:04.389000     0 days 03:27:38.638000   \n",
+       "std       1.155031  0 days 00:41:40.584927846  0 days 00:39:18.227030052   \n",
+       "\n",
+       "                     Sector1Time                Sector2Time  \\\n",
+       "count                       6056                       6599   \n",
+       "mean   0 days 00:00:32.801727873  0 days 00:00:44.382851038   \n",
+       "min       0 days 00:00:27.669000     0 days 00:00:37.715000   \n",
+       "25%       0 days 00:00:30.732750     0 days 00:00:41.676500   \n",
+       "50%       0 days 00:00:31.148000     0 days 00:00:42.582000   \n",
+       "75%       0 days 00:00:31.792000     0 days 00:00:43.779500   \n",
+       "max       0 days 00:01:39.160000     0 days 00:01:27.340000   \n",
+       "std    0 days 00:00:05.843587609  0 days 00:00:06.025195453   \n",
+       "\n",
+       "                     Sector3Time         Sector1SessionTime  ...  \\\n",
+       "count                       6566                       6048  ...   \n",
+       "mean   0 days 00:00:25.863896740  0 days 01:45:48.699285218  ...   \n",
+       "min       0 days 00:00:21.853000     0 days 00:15:57.525000  ...   \n",
+       "25%       0 days 00:00:23.736000     0 days 01:14:22.595250  ...   \n",
+       "50%       0 days 00:00:24.126000     0 days 01:44:32.815000  ...   \n",
+       "75%       0 days 00:00:24.901750     0 days 02:15:42.518250  ...   \n",
+       "max       0 days 00:01:10.478000     0 days 03:32:33.946000  ...   \n",
+       "std    0 days 00:00:04.862155415  0 days 00:42:50.934311129  ...   \n",
+       "\n",
+       "              Sector3SessionTime      SpeedI1      SpeedI2      SpeedFL  \\\n",
+       "count                       6566  5394.000000  6601.000000  5926.000000   \n",
+       "mean   0 days 01:42:19.752332013   221.138673   240.172095   277.101249   \n",
+       "min       0 days 00:15:28.005000    54.000000    44.000000    42.000000   \n",
+       "25%       0 days 01:09:21.996000   225.000000   241.000000   277.000000   \n",
+       "50%       0 days 01:40:39.423500   231.000000   250.000000   281.000000   \n",
+       "75%       0 days 02:14:05.441000   235.000000   257.000000   284.000000   \n",
+       "max       0 days 03:33:47.428000   248.000000   274.000000   302.000000   \n",
+       "std    0 days 00:44:32.425629872    28.861242    32.657155    22.133798   \n",
+       "\n",
+       "           SpeedST     TyreLife               LapStartTime  \\\n",
+       "count  5944.000000  6628.000000                       6628   \n",
+       "mean    276.023890     8.922299  0 days 01:40:00.888514634   \n",
+       "min      31.000000     1.000000     0 days 00:13:35.553000   \n",
+       "25%     280.000000     3.000000     0 days 01:06:55.995500   \n",
+       "50%     295.000000     8.000000     0 days 01:38:20.161000   \n",
+       "75%     303.000000    13.000000     0 days 02:12:04.575250   \n",
+       "max     333.000000    37.000000     0 days 03:32:00.121000   \n",
+       "std      52.878471     6.475231  0 days 00:44:57.137961013   \n",
+       "\n",
+       "                        LapStartDate     Position         Year  \n",
+       "count                           6622  5349.000000  6628.000000  \n",
+       "mean   2022-05-19 11:52:27.328777728     9.980183  2022.045112  \n",
+       "min       2020-11-28 14:00:03.421000     1.000000  2020.000000  \n",
+       "25%    2021-03-28 15:21:05.414749952     5.000000  2021.000000  \n",
+       "50%    2022-03-20 15:46:26.377999872    10.000000  2022.000000  \n",
+       "75%       2023-03-05 16:12:27.612000    15.000000  2023.000000  \n",
+       "max       2024-03-02 16:35:23.280000    20.000000  2024.000000  \n",
+       "std                              NaN     5.511766     1.411731  \n",
+       "\n",
+       "[8 rows x 21 columns]"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Time</th>\n",
+       "      <th>LapTime</th>\n",
+       "      <th>LapNumber</th>\n",
+       "      <th>Stint</th>\n",
+       "      <th>PitOutTime</th>\n",
+       "      <th>PitInTime</th>\n",
+       "      <th>Sector1Time</th>\n",
+       "      <th>Sector2Time</th>\n",
+       "      <th>Sector3Time</th>\n",
+       "      <th>Sector1SessionTime</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Sector3SessionTime</th>\n",
+       "      <th>SpeedI1</th>\n",
+       "      <th>SpeedI2</th>\n",
+       "      <th>SpeedFL</th>\n",
+       "      <th>SpeedST</th>\n",
+       "      <th>TyreLife</th>\n",
+       "      <th>LapStartTime</th>\n",
+       "      <th>LapStartDate</th>\n",
+       "      <th>Position</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>6628</td>\n",
+       "      <td>6038</td>\n",
+       "      <td>6628.000000</td>\n",
+       "      <td>6628.000000</td>\n",
+       "      <td>689</td>\n",
+       "      <td>694</td>\n",
+       "      <td>6056</td>\n",
+       "      <td>6599</td>\n",
+       "      <td>6566</td>\n",
+       "      <td>6048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6566</td>\n",
+       "      <td>5394.000000</td>\n",
+       "      <td>6601.000000</td>\n",
+       "      <td>5926.000000</td>\n",
+       "      <td>5944.000000</td>\n",
+       "      <td>6628.000000</td>\n",
+       "      <td>6628</td>\n",
+       "      <td>6622</td>\n",
+       "      <td>5349.000000</td>\n",
+       "      <td>6628.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0 days 01:42:02.192371303</td>\n",
+       "      <td>0 days 00:01:41.108333885</td>\n",
+       "      <td>24.366777</td>\n",
+       "      <td>2.545866</td>\n",
+       "      <td>0 days 01:07:15.479603773</td>\n",
+       "      <td>0 days 01:08:51.778342939</td>\n",
+       "      <td>0 days 00:00:32.801727873</td>\n",
+       "      <td>0 days 00:00:44.382851038</td>\n",
+       "      <td>0 days 00:00:25.863896740</td>\n",
+       "      <td>0 days 01:45:48.699285218</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 01:42:19.752332013</td>\n",
+       "      <td>221.138673</td>\n",
+       "      <td>240.172095</td>\n",
+       "      <td>277.101249</td>\n",
+       "      <td>276.023890</td>\n",
+       "      <td>8.922299</td>\n",
+       "      <td>0 days 01:40:00.888514634</td>\n",
+       "      <td>2022-05-19 11:52:27.328777728</td>\n",
+       "      <td>9.980183</td>\n",
+       "      <td>2022.045112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0 days 00:15:27.765000</td>\n",
+       "      <td>0 days 00:01:27.264000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0 days 00:13:35.553000</td>\n",
+       "      <td>0 days 00:18:28.415000</td>\n",
+       "      <td>0 days 00:00:27.669000</td>\n",
+       "      <td>0 days 00:00:37.715000</td>\n",
+       "      <td>0 days 00:00:21.853000</td>\n",
+       "      <td>0 days 00:15:57.525000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 00:15:28.005000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>42.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0 days 00:13:35.553000</td>\n",
+       "      <td>2020-11-28 14:00:03.421000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2020.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0 days 01:09:17.505500</td>\n",
+       "      <td>0 days 00:01:36.217000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0 days 00:29:59.107000</td>\n",
+       "      <td>0 days 00:35:37.662250</td>\n",
+       "      <td>0 days 00:00:30.732750</td>\n",
+       "      <td>0 days 00:00:41.676500</td>\n",
+       "      <td>0 days 00:00:23.736000</td>\n",
+       "      <td>0 days 01:14:22.595250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 01:09:21.996000</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>241.000000</td>\n",
+       "      <td>277.000000</td>\n",
+       "      <td>280.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0 days 01:06:55.995500</td>\n",
+       "      <td>2021-03-28 15:21:05.414749952</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2021.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0 days 01:39:58.302000</td>\n",
+       "      <td>0 days 00:01:37.859500</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0 days 00:59:30.380000</td>\n",
+       "      <td>0 days 00:58:21.241500</td>\n",
+       "      <td>0 days 00:00:31.148000</td>\n",
+       "      <td>0 days 00:00:42.582000</td>\n",
+       "      <td>0 days 00:00:24.126000</td>\n",
+       "      <td>0 days 01:44:32.815000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 01:40:39.423500</td>\n",
+       "      <td>231.000000</td>\n",
+       "      <td>250.000000</td>\n",
+       "      <td>281.000000</td>\n",
+       "      <td>295.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>0 days 01:38:20.161000</td>\n",
+       "      <td>2022-03-20 15:46:26.377999872</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0 days 02:13:43.248500</td>\n",
+       "      <td>0 days 00:01:40.345500</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0 days 01:28:09.343000</td>\n",
+       "      <td>0 days 01:25:50.796000</td>\n",
+       "      <td>0 days 00:00:31.792000</td>\n",
+       "      <td>0 days 00:00:43.779500</td>\n",
+       "      <td>0 days 00:00:24.901750</td>\n",
+       "      <td>0 days 02:15:42.518250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 02:14:05.441000</td>\n",
+       "      <td>235.000000</td>\n",
+       "      <td>257.000000</td>\n",
+       "      <td>284.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0 days 02:12:04.575250</td>\n",
+       "      <td>2023-03-05 16:12:27.612000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>2023.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0 days 03:33:47.428000</td>\n",
+       "      <td>0 days 00:03:05.092000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>0 days 03:28:04.389000</td>\n",
+       "      <td>0 days 03:27:38.638000</td>\n",
+       "      <td>0 days 00:01:39.160000</td>\n",
+       "      <td>0 days 00:01:27.340000</td>\n",
+       "      <td>0 days 00:01:10.478000</td>\n",
+       "      <td>0 days 03:32:33.946000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 03:33:47.428000</td>\n",
+       "      <td>248.000000</td>\n",
+       "      <td>274.000000</td>\n",
+       "      <td>302.000000</td>\n",
+       "      <td>333.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0 days 03:32:00.121000</td>\n",
+       "      <td>2024-03-02 16:35:23.280000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>2024.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0 days 00:44:32.891277624</td>\n",
+       "      <td>0 days 00:00:10.588901657</td>\n",
+       "      <td>16.860094</td>\n",
+       "      <td>1.155031</td>\n",
+       "      <td>0 days 00:41:40.584927846</td>\n",
+       "      <td>0 days 00:39:18.227030052</td>\n",
+       "      <td>0 days 00:00:05.843587609</td>\n",
+       "      <td>0 days 00:00:06.025195453</td>\n",
+       "      <td>0 days 00:00:04.862155415</td>\n",
+       "      <td>0 days 00:42:50.934311129</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0 days 00:44:32.425629872</td>\n",
+       "      <td>28.861242</td>\n",
+       "      <td>32.657155</td>\n",
+       "      <td>22.133798</td>\n",
+       "      <td>52.878471</td>\n",
+       "      <td>6.475231</td>\n",
+       "      <td>0 days 00:44:57.137961013</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.511766</td>\n",
+       "      <td>1.411731</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 21 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 49
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-20T03:40:23.364581Z",
+     "start_time": "2024-11-20T03:40:23.214723Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "#Visualizations\n",
+    "# Boxplot of Weather Data\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x='Year', y='TrackTemp', data=weather_data_combined)\n",
+    "plt.title('Temperature Distribution by Year')\n",
+    "plt.show()\n"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 52
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-20T04:00:21.344518Z",
+     "start_time": "2024-11-20T04:00:21.181130Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Graph of Fastest Lap Times by Year\n",
+    "# Who had the fastest lap time in each year?\n",
+    "fastest_lap = lap_data_combined[lap_data_combined['Position'] == 1]\n",
+    "# Remove 0 times\n",
+    "fastest_lap = fastest_lap[fastest_lap['Time'] != pd.Timedelta(0)]\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.lineplot(x='Year', y='Time', data=fastest_lap)\n",
+    "plt.title('Fastest Lap Times by Year')\n",
+    "plt.show()\n",
+    "\n"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 68
   }
  ],
  "metadata": {